4 Lessons from Successful Big Data Programs

Customer analytics is presented as the driving force behind big data success in new survey results.

Insurance Networking News, January 28, 2013

Justin Kern

To get the most out of big data programs, early adopters have found success through a mix of nimble analytic practices and tried-and-true business-first planning, according to a new report from Aberdeen Research.

In “Data Management for BI: Getting Accurate Decisions from Big Data,” Aberdeen analyst Nathaniel Rowe assessed survey results from 125 organizations at some stage in their big data programs. Rowe says that customer analytics has become “the driving force behind big data developments,” so it’s of little surprise that many of the “best-in-class” enterprises in the report came from the fields of retail and telecommunications. Rowe also said specific use cases are popping up as having big, quick returns, like fraud detection in financial services or the analysis of sensor data in utilities.

Regardless of the industry, Rowe and Aberdeen found four principles that rang true in all successful advanced analytics initiatives.

Support a data-driven culture. Of best-in-class enterprises in the report, which represented one-fifth of the total surveyed, 93 percent said they wholly relied on their data quality. In turn, they reported a 35 percent year-over-year increase in accessible business data with 91 percent of their key business data items delivered on time. Fifty-seven percent of “laggard” enterprises, which took up 30 percent of those surveyed, said they trusted their data from the start, according to Aberdeen.

Develop or hire skilled staff. The most successful enterprises opted for in-house analytic training and/or bringing in a specialized data scientist or analyst as part of a big data program. “Standardized training on basic data management, data creation, data discipline and new data systems are certainly marks of Best-in-Class performance. The [best-in-class] are also five-times more likely than laggards to have at least one data scientist on board,” among those surveyed, Rowe said.

Build a flexible, high-speed data infrastructure. It’s no doubt easier said than done, particularly if you’re juggling legacy systems and other BI initiatives. But the highly successful advanced analytics initiatives were able to get it done, Aberdeen stressed. Big data programs that worked across an enterprise generally featured data warehouses fed in real-time, from a range of structured and unstructured sources.

Enable self-service analytics. As part of the planning process, Aberdeen recommended sewing in tools for widespread analytic exploration and drill-downs, even if everyone can’t jump on board right away. Adding this to the expectations of big data imparts more enterprise data agility and can take away some of the burden of reports and data crunching on IT, according to the research firm.

Click here to access the report.

This story originally appeared at Information Management.

For more information on related topics, visit the following channels:

• Customer Service
• Data Management
• Enterprise Technologies